In the era of personalized education, the provision of comprehensible
explanations for learning recommendations is of a great value to enhance the
learner's understanding and engagement with the recommended learning content.
Large language models (LLMs) and generative AI in general have recently opened
new doors for generating human-like explanations, for and along learning
recommendations. However, their precision is still far away from acceptable in
a sensitive field like education. To harness the abilities of LLMs, while still
ensuring a high level of precision towards the intent of the learners, this
paper proposes an approach to utilize knowledge graphs (KG) as a source of
factual context, for LLM prompts, reducing the risk of model hallucinations,
and safeguarding against wrong or imprecise information, while maintaining an
application-intended learning context. We utilize the semantic relations in the
knowledge graph to offer curated knowledge about learning recommendations. With
domain-experts in the loop, we design the explanation as a textual template,
which is filled and completed by the LLM. Domain experts were integrated in the
prompt engineering phase as part of a study, to ensure that explanations
include information that is relevant to the learner. We evaluate our approach
quantitatively using Rouge-N and Rouge-L measures, as well as qualitatively
with experts and learners. Our results show an enhanced recall and precision of
the generated explanations compared to those generated solely by the GPT model,
with a greatly reduced risk of generating imprecise information in the final
learning explanation.

在个性化教育时代，为学习推荐提供易于理解的解释对增强学习者的理解和参与推荐的学习内容具有巨大价值。本文提出了一种利用知识图谱作为意见引擎源的方法，以减小语言模型模型的错误生成的风险，并确保高精度地满足学习者的意图，同时保持有关应用的学习上下文。我们利用知识图谱中的语义关系提供关于学习推荐的精心策划的知识。通过将领域专家纳入研究的提示工程阶段，设计解释模板，由语言模型进行填充和完成来确保解释包含与学习者相关的信息。我们定量评估了我们的方法使用 Rouge-N 和 Rouge-L 指标，并进行了与专家和学习者的质性评估。我们的结果表明，与仅由 GPT 模型生成的解释相比，生成的解释具有更高的召回率和准确度，并且极大地降低了最终学习解释中生成不准确信息的风险。